Dynamic power management is a technique to reduce power consumption of electronic systems by selectively shutting down idle components. In this paper, an intelligent approach is presented based on reinforcement learning to predict the best policy amongst the existing DPM policies. Reinforcement learning is a computational approach to understanding and automating goal-directed learning and decision-making. The effectiveness of this approach is demonstrated by an event-driven simulator, which is designed using JAVA for power-manageable embedded devices. The results of the experiments conducted in this regard establish that the proposed DPM scheme enhances power savings by 10 to 28%.
Investigation of LSTM based prediction for dynamic energy management in chip multiprocessors
